From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030
- URL: http://arxiv.org/abs/2405.12731v2
- Date: Fri, 27 Sep 2024 13:17:57 GMT
- Title: From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030
- Authors: Ketai Qiu, Niccolò Puccinelli, Matteo Ciniselli, Luca Di Grazia,
- Abstract summary: We provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030.
We envision HyperAssistant, an augmented AI tool that offers comprehensive support to 2030 developers.
- Score: 3.437372707846067
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towards AI-assisted programming, exemplified by tools like GitHub Copilot and OpenAI's ChatGPT, which have become a crucial element for coding, debugging, and software design. In this paper we provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030, by exploring how AI advancements are set to enhance the implementation phase, fundamentally altering developers' roles from manual coders to orchestrators of AI-driven development ecosystems. We envision HyperAssistant, an augmented AI tool that offers comprehensive support to 2030 developers, addressing current limitations in mental health support, fault detection, code optimization, team interaction, and skill development. We emphasize AI as a complementary force, augmenting developers' capabilities rather than replacing them, leading to the creation of sophisticated, reliable, and secure software solutions. Our vision seeks to anticipate the evolution of programming practices, challenges, and future directions, shaping a new paradigm where developers and AI collaborate more closely, promising a significant leap in SE efficiency, security and creativity.
Related papers
- How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering [8.65285948382426]
We propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types.
Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development.
arXiv Detail & Related papers (2025-01-15T12:53:49Z) - From Defects to Demands: A Unified, Iterative, and Heuristically Guided LLM-Based Framework for Automated Software Repair and Requirement Realization [44.99833362998488]
This manuscript signals a new era in the integration of artificial intelligence with software engineering.
We present a formalized, iterative methodology proving that AI can fully replace human programmers in all aspects of code creation and refinement.
arXiv Detail & Related papers (2024-12-06T14:54:21Z) - Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap [30.996760992473064]
Software Engineering 3.0 (SE 3.0) is an AI-native approach characterized by intent-first, conversation-oriented development.
We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership.
This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.
arXiv Detail & Related papers (2024-10-08T15:04:07Z) - "I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development [19.851794567529286]
This study investigates the adoption, impact, and security considerations of AI-empowered tools in the software development process.
Our findings reveal widespread adoption of AI tools across various stages of software development.
arXiv Detail & Related papers (2024-09-20T09:17:10Z) - OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.
We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers [30.996760992473064]
We propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers.
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
arXiv Detail & Related papers (2024-04-16T02:10:20Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - The GitHub Development Workflow Automation Ecosystems [47.818229204130596]
Large-scale software development has become a highly collaborative endeavour.
This chapter explores the ecosystems of development bots and GitHub Actions.
It provides an extensive survey of the state-of-the-art in this domain.
arXiv Detail & Related papers (2023-05-08T15:24:23Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.